Visualization with GGPlot

 

Rodney Dyer, PhD

Learning Objectives

  • Set up online repositories for data such as Google Drive and retrieve it directly into RStudio for utilization.

  • Describe how the Grammar of Graphics deviates from built-in plotting commands.

  • Use an aes-thetic to define which data components of the data will be used in constructing the graphic.

  • Apply geommetric data layers to existing plots.

Data

For this example, we will use the Rice Rivers Center data again.

library( tidyverse )
url <- "https://raw.githubusercontent.com/dyerlab/ENVS-Lectures/master/data/arapat.csv"
data <- read_csv(url)

The Grammar of Graphics

 

 

Components of Graphical Objects

  • A aesthetic statement indicating which columns of data to use and how to use them in the plot (designating x-axis vs color, etc.).

  • An estimate of a trendline through the data (the red one), which displays a statistical summary of the raw data.

  • A set of geometric overlays for the points which include size and shape configurations.

  • Specified color scheme for the regions.

  • Labeling of a subset of the data (which is done using a separate data.frame derived from the first).

  • Labels on axes.

  • A legend positioned in a specific fashion.

  • A title over the whole thing.

  • A theme for the rest of the coloring and customized lines and grids.

 

The Grammar of Graphics

  • Data

  • Aesthetics

  • Transformations

  • Partitions

  • Auxillary Text

  • Overlays

The ggplot2 Library

R Packages for Data Science

  • RStudio + Hadley Wickham

  • Collection of Packages

  • Makes you AWESOME

library( ggplot2 )

The Aesthetics

An aesthetic is a function that allows you to tell the graphics which columns of data are to be used in the creation of graph features.

aes( x = Sepal.Length, y = Sepal.Width )
Aesthetic mapping: 
* `x` -> `Sepal.Length`
* `y` -> `Sepal.Width`

 

Commonly included within the initial call to ggplot()

ggplot( iris, aes( x = Sepal.Length, y = Sepal.Width ) )

Stepwise Creation of a Plot

ggplot( iris )

Stepwise Creation of a Plot

ggplot( iris, aes( x = Sepal.Length) )

Stepwise Creation of a Plot

Adding a Geometry Layer

ggplot( iris, aes( x = Sepal.Length) ) + 
  geom_histogram()

Stepwise Creation of a Density Plot

ggplot( iris, aes( x = Sepal.Length) ) +
  geom_density()

A Scatter Plot

ggplot( iris, aes( x = Sepal.Length, y = Sepal.Width) ) + geom_point()

Scatterplot with Colors

Aesthetics also contribute to symbologies and colors

ggplot( iris, aes( x = Sepal.Length, 
                   y = Sepal.Width, 
                   color=Species) ) + 
  geom_point()

In & Out of aes()

ggplot( iris ) + 
  geom_point(aes( x = Sepal.Length, y = Sepal.Width, col=Species), shape=5)

Iterative Building of Graphics

p <- ggplot( iris )
p <- p + geom_point( aes( x = Sepal.Length, 
                          y = Sepal.Width, 
                          col=Species, 
                          shape=Species), 
                     size=3, 
                     alpha=0.75 ) 
p <- p + xlab("Sepal Length")
p <- p + ylab("Sepal Length")
class(p)
[1] "gg"     "ggplot"

Printing out p

p

Scope of Visibility

Only things in ggplot() apply to all following components. Placing aes() or data= parts in later components only make them visible to that particular component.

ggplot( iris, aes( x = Sepal.Length, y = Sepal.Width) ) + 
  geom_point() + 
  stat_smooth() 
ggplot( iris ) + 
  geom_point( aes( x = Sepal.Length, y = Sepal.Width) ) + 
  stat_smooth(  aes( x = Sepal.Length, y = Sepal.Width) )
ggplot() + 
  geom_point( aes( x = Sepal.Length, y = Sepal.Width), data = iris )  + 
  stat_smooth( aes( x = Sepal.Length, y = Sepal.Width), data = iris )

Themes

Customizable!!!

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

p + theme_bw()

 

p + theme_bw()

 

p + theme_gray()

 

p + theme_linedraw()

 

p + theme_dark()

 

p + theme_minimal()

 

p + theme_classic()

 

p + theme_void()

Create Your Own Themes

source("theme_dyerlab_grey.R")
theme_dyerlab_grey
function (base_size = 18, base_family = "") 
{
    theme_grey(base_size = base_size, base_family = base_family) %+replace% 
        theme(axis.line = element_blank(), axis.text.x = element_text(size = base_size * 
            0.8, color = "white", lineheight = 0.9), axis.text.y = element_text(size = base_size * 
            0.8, color = "white", lineheight = 0.9), axis.ticks = element_line(color = "white", 
            size = 0.2), axis.title.x = element_text(size = base_size, 
            color = "white", margin = margin(0, 10, 0, 0)), axis.title.y = element_text(size = base_size, 
            color = "white", angle = 90, margin = margin(0, 10, 
                0, 0)), axis.ticks.length = unit(0.3, "lines"), 
            legend.background = element_rect(color = NA, fill = "#272822"), 
            legend.key = element_rect(color = "white", fill = "#272822"), 
            legend.key.size = unit(1.2, "lines"), legend.key.height = NULL, 
            legend.key.width = NULL, legend.text = element_text(size = base_size * 
                0.8, color = "white"), legend.title = element_text(size = base_size * 
                0.8, face = "bold", hjust = 0, color = "white"), 
            legend.position = "right", legend.text.align = NULL, 
            legend.title.align = NULL, legend.direction = "vertical", 
            legend.box = NULL, panel.background = element_rect(fill = "#272822", 
                color = NA), panel.border = element_rect(fill = NA, 
                color = "white"), panel.grid.major = element_line(color = "grey35"), 
            panel.grid.minor = element_line(color = "grey20"), 
            panel.spacing = unit(0.5, "lines"), strip.background = element_rect(fill = "grey30", 
                color = "grey10"), strip.text.x = element_text(size = base_size * 
                0.8, color = "white"), strip.text.y = element_text(size = base_size * 
                0.8, color = "white", angle = -90), plot.background = element_rect(color = "#272822", 
                fill = "#272822"), plot.title = element_text(size = base_size * 
                1.2, color = "white"), plot.margin = unit(rep(1, 
                4), "lines"))
}

Create Your Own Themes

p + theme_dyerlab_grey()

Font Sizes

p + theme_dyerlab_grey( base_size = 22)

Globally Set Font Sizes

You can also set the theme and base sizes (and other theme-related items) globally for an entire document/presentation as:

ggplot2::theme_set( theme_bw( base_size=16) )

Boxplots

ggplot( iris, aes( x = Sepal.Length) ) + 
  geom_boxplot( notch=TRUE )

Species Differences

ggplot( iris, aes(x=Species, y=Sepal.Length) )  + 
  geom_boxplot( notch=TRUE )

Species Differences Fill Colors

ggplot( iris, aes(x=Species, y=Sepal.Length) )  + 
  geom_boxplot( notch=TRUE, fill=c("#002145") ) +
  ylab("Sepal Length")

Species Differences Fill Colors

ggplot( iris, aes(x=Species, y=Sepal.Length) )  + 
  geom_boxplot( notch=TRUE, fill=c("#002145", "#a5acaf","#66c010") ) +
  ylab("Sepal Length")

Overlays

Overlaying a Trendline

ggplot( iris, aes( x = Sepal.Length, y = Sepal.Width) ) + 
  geom_point()  + 
  stat_smooth()

Overlaying a Trendline

ggplot( iris, aes( x = Sepal.Length, y = Sepal.Width) ) + 
  geom_point()  + 
  stat_smooth( method="lm", formula = "y ~ x")

Stacking Order

ggplot( iris, aes( x = Sepal.Length, y = Sepal.Width) ) + 
  geom_point( color="red")  + 
  stat_smooth( fill="black", alpha=1)

Stacking Order

ggplot( iris, aes( x = Sepal.Length, y = Sepal.Width) ) + 
  stat_smooth( fill="black", alpha=1) + 
  geom_point( color="red")

On-The-Fly Transformations

Customizing the y-axis data format…

ggplot( iris, aes(x = Sepal.Length) ) + 
  geom_histogram( aes( y = ..density.. ), 
                  color="green", 
                  fill="orange", bins = 15 ) + 
  geom_density( color = "magenta", lwd=1.5 )

Textual Overlays

cor_model <- cor.test( iris$Sepal.Length, iris$Sepal.Width)
cor_model

    Pearson's product-moment correlation

data:  iris$Sepal.Length and iris$Sepal.Width
t = -1.4403, df = 148, p-value = 0.1519
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.27269325  0.04351158
sample estimates:
       cor 
-0.1175698 
names( cor_model ) 
[1] "statistic"   "parameter"   "p.value"     "estimate"    "null.value" 
[6] "alternative" "method"      "data.name"   "conf.int"

Formatting as Text

cor.text <- paste( "r = ", 
                   format( cor_model$estimate, digits=4), 
                   ";\n P = ", 
                   format( cor_model$p.value, digits=4 ), 
                   sep="" ) 
cor.text
[1] "r = -0.1176;\n P = 0.1519"

 

p + geom_text( aes(x=7.25, y=4.25, label=cor.text) )

Labels

sLength <- by( iris$Sepal.Length, iris$Species, mean )
sWidth <- by( iris$Sepal.Width, iris$Species, mean )

df <- data.frame( Sepal.Length = as.numeric( sLength ),
                  Sepal.Width = as.numeric( sWidth ),
                  Species = levels( iris$Species ) )
df
  Sepal.Length Sepal.Width    Species
1        5.006       3.428     setosa
2        5.936       2.770 versicolor
3        6.588       2.974  virginica

Labels

ggplot( iris, aes(Sepal.Length, Sepal.Width) ) + 
  geom_point( aes(color=Species) ) + 
  geom_text( aes(label=Species), data=df)

Smart Labels

library( ggrepel )

 

ggplot( iris, aes(Sepal.Length, Sepal.Width) ) + 
  geom_point( aes(color=Species) ) + 
  geom_label_repel( aes(label=Species), data=df )

Remove Legend

ggplot( iris, aes(Sepal.Length, Sepal.Width) ) + 
  geom_point( aes(color=Species) ) + 
  geom_label_repel( aes(label=Species), data=df ) + 
  guides( color = FALSE )

Questions

If you have any questions, please feel free to either post them as an “Issue” on your copy of this GitHub Repository, post to the Canvas discussion board for the class, or drop me an email.

Peter Sellers looking bored

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